Backfills in Data & Machine Learning: A Primer

A backfill is a process in data engineering where historical parts of a data asset are updated or filled in using incremental updates, typically to maintain consistency and accuracy. Backfills are often necessary when changes are made to the underlying data source or code that generates the data, or when new data assets are added to a pipeline. The process can be complex and requires careful planning, execution, and monitoring to avoid issues such as resource overload, cost overload, and getting lost in the middle. Using partitions to organize data can make backfills easier by allowing for parallel processing and tracking of dependencies between data assets. A step-by-step guide for running a backfill includes managing data organization, planning, launching, monitoring, and verifying results.

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